We retrained our method on breast cancer dataset to make a direct comparison to all the context-aware approaches presented in [1]}. Sirinukunwattana et al. used a customized network as feature extractor which contains 5 convolution layers with \(4\times 4\)  filter and each layer followed by batch-norm and leaky-ReLU activation. To highlight the significance of RA-CNN, we adopted their network for LR-CNN and trained our RA-CNN 3 with standard training strategy in end-to-end training manner. Moreover, the same experimental setup (as in [1]}) has been used for retraining of our method such as data-splits and F1-score based Rank-sum evaluation metric. Table REF  presents the comparison of our proposed method with ten other context-aware approaches. Our method outperformed its counterpart methods with significant margin and achieved best rank-sum scored for breast cancer classification task as well.
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